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Update app.py
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app.py
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@@ -10,6 +10,8 @@ import PyPDF2
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import os
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import json
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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@@ -44,12 +46,21 @@ class SmartShoppingAssistant:
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)
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]
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agent = create_openai_functions_agent(
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llm=
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tools=tools,
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prompt=
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)
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self.agent_executor = AgentExecutor(
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@@ -85,21 +96,6 @@ class SmartShoppingAssistant:
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return "\n\n".join(results) if results else "No matching products found."
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except Exception as e:
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return f"Error processing query: {str(e)}"
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def extract_text_from_image(self, image):
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try:
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response = model_flash_think.generate_content(image)
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return response.text
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except Exception as e:
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return f"Error processing image: {str(e)}"
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def extract_text_from_pdf(self, pdf_file):
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try:
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pdf_reader = PyPDF2.PdfReader(pdf_file)
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text = "".join([page.extract_text() for page in pdf_reader.pages])
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return text
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except Exception as e:
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return f"Error processing PDF: {str(e)}"
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def main():
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st.set_page_config(page_title="Smart Shopping Assistant", layout="wide")
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import os
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import json
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from dotenv import load_dotenv
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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# Load environment variables
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load_dotenv()
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)
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]
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# Define a prompt template for the agent
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prompt_template = """
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You are a shopping assistant. Use the tools provided to find products based on user queries.
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{input}
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"""
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# Create a PromptTemplate and LLMChain to make the model compatible
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prompt = PromptTemplate(input_variables=["input"], template=prompt_template)
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llm_chain = LLMChain(llm=llm_flash_exp, prompt=prompt)
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# Create the agent with the adapted chain
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agent = create_openai_functions_agent(
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llm=llm_chain,
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tools=tools,
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prompt=prompt_template
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)
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self.agent_executor = AgentExecutor(
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return "\n\n".join(results) if results else "No matching products found."
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except Exception as e:
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return f"Error processing query: {str(e)}"
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def main():
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st.set_page_config(page_title="Smart Shopping Assistant", layout="wide")
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